OpenRaiser/Intern-Atlas huggingface dataset:
Digital Twin-Empowered Cooperative Autonomous Car-sharing Services: Proof-of-Concept
Venue: arXiv cs.OH 2025
Year: 2025

This paper presents a digital twin-empowered real-time optimal delivery system specifically validated through a proof-of-concept (PoC) demonstration of a real-world autonomous car-sharing service. This study integrates real-time data from roadside units (RSUs) and connected and autonomous vehicles (CAVs) within a digital twin of a campus environment to address the dynamic challenges of urban traffic. The proposed system leverages the Age of Information (AoI) metric to optimize vehicle routing by maintaining data freshness and dynamically adapting to real-time traffic conditions. Experimental results from the PoC demonstrate a 22% improvement in delivery efficiency compared to conventional shortest-path methods that do not consider information freshness. Furthermore, digital twin-based simulation results demonstrate that this proposed system improves overall delivery efficiency by 12% and effectively reduces the peak average AoI by 23% compared to the conventional method, where each vehicle selects the shortest route without considering information freshness. This study confirms the practical feasibility of cooperative driving systems, highlighting their potential to enhance smart mobility solutions through scalable digital twin deployments in complex urban environments.
Identification over Poisson ISI Channels: Feedback and Molecular Applications
Venue: arXiv cs.IT 2025
Year: 2025

Molecular communication (MC) enables information transfer via molecules, making it ideal for biomedical applications where traditional methods fall short. In many such scenarios, identifying specific events is more critical than decoding full messages, motivating the use of deterministic identification (DI). This paper investigates DI over discrete-time Poisson channels (DTPCs) with inter-symbol interference (ISI), a realistic setting due to channel memory effects. We improve the known upper bound on DI capacity under power constraints from $\frac{3}{2} + κ$ to $\frac{1 + κ}{2}$. Additionally, we present the first results on deterministic identification with feedback (DIF) in this context, providing a constructive lower bound. These findings enhance the theoretical understanding of MC and support more efficient, feedback-driven biomedical systems.
From regular expressions to deterministic finite automata: $2^{\frac{n}{2}+\sqrt{n}(\log n)^{Θ(1)}}$ states are necessary and sufficient
Venue: arXiv cs.FL 2025
Year: 2025

It is proved that every regular expression of alphabetic width $n$, that is, with $n$ occurrences of symbols of the alphabet, can be transformed into a deterministic finite automaton (DFA) with $2^{\frac{n}{2}+(\frac{\log_2 e}{2\sqrt{2}}+o(1))\sqrt{n\ln n}}$ states recognizing the same language (the best upper bound up to date is $2^n$). At the same time, it is also shown that this bound is close to optimal, namely, that there exist regular expressions of alphabetic width $n$ over a two-symbol alphabet, such that every DFA for the same language has at least $2^{\frac{n}{2}+(\sqrt{2} + o(1))\sqrt{\frac{n}{\ln n}}}$ states (the previously known lower bound is $\frac{5}{4}2^{\frac{n}{2}}$). The same bounds are obtained for an intermediate problem of determinizing nondetermistic finite automata (NFA) with each state having all incoming transitions by the same symbol.
Purifying, Labeling, and Utilizing: A High-Quality Pipeline for Small Object Detection
Venue: arXiv cs.CV 2025
Year: 2025

Small object detection is a broadly investigated research task and is commonly conceptualized as a "pipeline-style" engineering process. In the upstream, images serve as raw materials for processing in the detection pipeline, where pre-trained models are employed to generate initial feature maps. In the midstream, an assigner selects training positive and negative samples. Subsequently, these samples and features are fed into the downstream for classification and regression. Previous small object detection methods often focused on improving isolated stages of the pipeline, thereby neglecting holistic optimization and consequently constraining overall performance gains. To address this issue, we have optimized three key aspects, namely Purifying, Labeling, and Utilizing, in this pipeline, proposing a high-quality Small object detection framework termed PLUSNet. Specifically, PLUSNet comprises three sequential components: the Hierarchical Feature Purifier (HFP) for purifying upstream features, the Multiple Criteria Label Assignment (MCLA) for improving the quality of midstream training samples, and the Frequency Decoupled Head (FDHead) for more effectively exploiting information to accomplish downstream tasks. The proposed PLUS modules are readily integrable into various object detectors, thus enhancing their detection capabilities in multi-scale scenarios. Extensive experiments demonstrate the proposed PLUSNet consistently achieves significant and consistent improvements across multiple datasets for small object detection.
EfficientHuman: Efficient Training and Reconstruction of Moving Human using Articulated 2D Gaussian
Venue: arXiv cs.CV 2025
Year: 2025

3D Gaussian Splatting (3DGS) has been recognized as a pioneering technique in scene reconstruction and novel view synthesis. Recent work on reconstructing the 3D human body using 3DGS attempts to leverage prior information on human pose to enhance rendering quality and improve training speed. However, it struggles to effectively fit dynamic surface planes due to multi-view inconsistency and redundant Gaussians. This inconsistency arises because Gaussian ellipsoids cannot accurately represent the surfaces of dynamic objects, which hinders the rapid reconstruction of the dynamic human body. Meanwhile, the prevalence of redundant Gaussians means that the training time of these works is still not ideal for quickly fitting a dynamic human body. To address these, we propose EfficientHuman, a model that quickly accomplishes the dynamic reconstruction of the human body using Articulated 2D Gaussian while ensuring high rendering quality. The key innovation involves encoding Gaussian splats as Articulated 2D Gaussian surfels in canonical space and then transforming them to pose space via Linear Blend Skinning (LBS) to achieve efficient pose transformations. Unlike 3D Gaussians, Articulated 2D Gaussian surfels can quickly conform to the dynamic human body while ensuring view-consistent geometries. Additionally, we introduce a pose calibration module and an LBS optimization module to achieve precise fitting of dynamic human poses, enhancing the model's performance. Extensive experiments on the ZJU-MoCap dataset demonstrate that EfficientHuman achieves rapid 3D dynamic human reconstruction in less than a minute on average, which is 20 seconds faster than the current state-of-the-art method, while also reducing the number of redundant Gaussians.
Multi-Sensor Fusion for Quadruped Robot State Estimation using Invariant Filtering and Smoothing
Venue: arXiv cs.RO 2025
Year: 2025

This letter introduces two multi-sensor state estimation frameworks for quadruped robots, built on the Invariant Extended Kalman Filter (InEKF) and Invariant Smoother (IS). The proposed methods, named E-InEKF and E-IS, fuse kinematics, IMU, LiDAR, and GPS data to mitigate position drift, particularly along the z-axis, a common issue in proprioceptive-based approaches. We derived observation models that satisfy group-affine properties to integrate LiDAR odometry and GPS into InEKF and IS. LiDAR odometry is incorporated using Iterative Closest Point (ICP) registration on a parallel thread, preserving the computational efficiency of proprioceptive-based state estimation. We evaluate E-InEKF and E-IS with and without exteroceptive sensors, benchmarking them against LiDAR-based odometry methods in indoor and outdoor experiments using the KAIST HOUND2 robot. Our methods achieve lower Relative Position Errors (RPE) and significantly reduce Absolute Trajectory Error (ATE), with improvements of up to 28% indoors and 40% outdoors compared to LIO-SAM and FAST-LIO2. Additionally, we compare E-InEKF and E-IS in terms of computational efficiency and accuracy.
DiffusionRIR: Room Impulse Response Interpolation using Diffusion Models
Venue: arXiv cs.SD 2025
Year: 2025

Room Impulse Responses (RIRs) characterize acoustic environments and are crucial in multiple audio signal processing tasks. High-quality RIR estimates drive applications such as virtual microphones, sound source localization, augmented reality, and data augmentation. However, obtaining RIR measurements with high spatial resolution is resource-intensive, making it impractical for large spaces or when dense sampling is required. This research addresses the challenge of estimating RIRs at unmeasured locations within a room using Denoising Diffusion Probabilistic Models (DDPM). Our method leverages the analogy between RIR matrices and image inpainting, transforming RIR data into a format suitable for diffusion-based reconstruction. Using simulated RIR data based on the image method, we demonstrate our approach's effectiveness on microphone arrays of different curvatures, from linear to semi-circular. Our method successfully reconstructs missing RIRs, even in large gaps between microphones. Under these conditions, it achieves accurate reconstruction, significantly outperforming baseline Spline Cubic Interpolation in terms of Normalized Mean Square Error and Cosine Distance between actual and interpolated RIRs. This research highlights the potential of using generative models for effective RIR interpolation, paving the way for generating additional data from limited real-world measurements.
A Novel Cipher for Enhancing MAVLink Security: Design, Security Analysis, and Performance Evaluation Using a Drone Testbed
Venue: arXiv cs.CR 2025
Year: 2025

We present MAVShield, a novel lightweight cipher designed to secure communications in Unmanned Aerial Vehicles (UAVs) using the MAVLink protocol, which by default transmits unencrypted messages between UAVs and Ground Control Stations (GCS). While existing studies propose encryption for MAVLink, most remain theoretical or simulation-based. We implement MAVShield alongside AES-CTR, ChaCha20, Speck-CTR, and Rabbit, and evaluate them on a real drone testbed. A comprehensive security analysis using statistical test suites (NIST and Diehard) demonstrates strong resistance of the novel cipher to cryptanalysis. Performance evaluation across key metrics including memory usage, CPU load, and battery power consumption, demonstrates that MAVShield outperforms existing algorithms and offers an efficient, real-world solution for securing MAVLink communications in UAVs.
Explanations Go Linear: Interpretable and Individual Latent Encoding for Post-hoc Explainability
Venue: arXiv cs.LG 2025
Year: 2025

Post-hoc explainability is essential for understanding black-box machine learning models. Surrogate-based techniques are widely used for local and global model-agnostic explanations but have significant limitations. Local surrogates capture non-linearities but are computationally expensive and sensitive to parameters, while global surrogates are more efficient but struggle with complex local behaviors. In this paper, we present ILLUME, a flexible and interpretable framework grounded in representation learning, that can be integrated with various surrogate models to provide explanations for any black-box classifier. Specifically, our approach combines a globally trained surrogate with instance-specific linear transformations learned with a meta-encoder to generate both local and global explanations. Through extensive empirical evaluations, we demonstrate the effectiveness of ILLUME in producing feature attributions and decision rules that are not only accurate but also robust and faithful to the black-box, thus providing a unified explanation framework that effectively addresses the limitations of traditional surrogate methods.
Can LLMs Detect Intrinsic Hallucinations in Paraphrasing and Machine Translation?
Venue: arXiv cs.CL 2025
Year: 2025

A frequently observed problem with LLMs is their tendency to generate output that is nonsensical, illogical, or factually incorrect, often referred to broadly as hallucination. Building on the recently proposed HalluciGen task for hallucination detection and generation, we evaluate a suite of open-access LLMs on their ability to detect intrinsic hallucinations in two conditional generation tasks: translation and paraphrasing. We study how model performance varies across tasks and language and we investigate the impact of model size, instruction tuning, and prompt choice. We find that performance varies across models but is consistent across prompts. Finally, we find that NLI models perform comparably well, suggesting that LLM-based detectors are not the only viable option for this specific task.
BrightCookies at SemEval-2025 Task 9: Exploring Data Augmentation for Food Hazard Classification
Venue: arXiv cs.CL 2025
Year: 2025

This paper presents our system developed for the SemEval-2025 Task 9: The Food Hazard Detection Challenge. The shared task's objective is to evaluate explainable classification systems for classifying hazards and products in two levels of granularity from food recall incident reports. In this work, we propose text augmentation techniques as a way to improve poor performance on minority classes and compare their effect for each category on various transformer and machine learning models. We explore three word-level data augmentation techniques, namely synonym replacement, random word swapping, and contextual word insertion. The results show that transformer models tend to have a better overall performance. None of the three augmentation techniques consistently improved overall performance for classifying hazards and products. We observed a statistically significant improvement (P < 0.05) in the fine-grained categories when using the BERT model to compare the baseline with each augmented model. Compared to the baseline, the contextual words insertion augmentation improved the accuracy of predictions for the minority hazard classes by 6%. This suggests that targeted augmentation of minority classes can improve the performance of transformer models.
Beyond the Last Answer: Your Reasoning Trace Uncovers More than You Think
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) leverage step-by-step reasoning to solve complex problems. Standard evaluation practice involves generating a complete reasoning trace and assessing the correctness of the final answer presented at its conclusion. In this paper, we challenge the reliance on the final answer by posing the following two questions: Does the final answer reliably represent the model's optimal conclusion? Can alternative reasoning paths yield different results? To answer these questions, we analyze intermediate reasoning steps, termed subthoughts, and propose a method based on our findings. Our approach involves segmenting a reasoning trace into sequential subthoughts based on linguistic cues. We start by prompting the model to generate continuations from the end-point of each intermediate subthought. We extract a potential answer from every completed continuation originating from different subthoughts. We find that aggregating these answers by selecting the most frequent one (the mode) often yields significantly higher accuracy compared to relying solely on the answer derived from the original complete trace. Analyzing the consistency among the answers derived from different subthoughts reveals characteristics that correlate with the model's confidence and correctness, suggesting potential for identifying less reliable answers. Our experiments across various LLMs and challenging mathematical reasoning datasets (AIME2024 and AIME2025) show consistent accuracy improvements, with gains reaching up to 13\% and 10\% respectively. Implementation is available at: https://github.com/hammoudhasan/SubthoughtReasoner.
On the Effect of Time Preferences on the Price of Anarchy
Venue: arXiv cs.GT 2025
Year: 2025

This paper examines the impact of agents' myopic optimization on the efficiency of systems comprised by many selfish agents. In contrast to standard congestion games where agents interact in a one-shot fashion, in our model each agent chooses an infinite sequence of actions and maximizes the total reward stream discounted over time under different ways of computing present values. Our model assumes that actions consume common resources that get congested, and the action choice by an agent affects the completion times of actions chosen by other agents, which in turn affects the time rewards are accrued and their discounted value. This is a mean-field game, where an agent's reward depends on the decisions of the other agents through the resulting action completion times. For this type of game we define stationary equilibria, and analyze their existence and price of anarchy (PoA). Overall, we find that the PoA depends entirely on the type of discounting rather than its specific parameters. For exponential discounting, myopic behaviour leads to extreme inefficiency: the PoA is infinity for any value of the discount parameter. For power law discounting, such inefficiency is greatly reduced and the PoA is 2 whenever stationary equilibria exist. This matches the PoA when there is no discounting and players maximize long-run average rewards. Additionally, we observe that exponential discounting may introduce unstable equilibria in learning algorithms, if action completion times are interdependent. In contrast, under no discounting all equilibria are stable.
Deterministic Dynamic Maximal Matching in Sublinear Update Time
Venue: arXiv cs.DS 2025
Year: 2025

We give a fully dynamic deterministic algorithm for maintaining a maximal matching of an $n$-vertex graph in $\tilde{O}(n^{8/9})$ amortized update time. This breaks the long-standing $Ω(n)$-update-time barrier on dense graphs, achievable by trivially scanning all incident vertices of the updated edge, and affirmatively answers a major open question repeatedly asked in the literature [BGS15, BCHN18, Sol22]. We also present a faster randomized algorithm against an adaptive adversary with $\tilde{O}(n^{3/4})$ amortized update time. Our approach employs the edge degree constrained subgraph (EDCS), a central object for optimizing approximation ratio, in a completely novel way; we instead use it for maintaining a matching that matches all high degree vertices in sublinear update time so that it remains to handle low degree vertices rather straightforwardly. To optimize this approach, we employ tools never used in the dynamic matching literature prior to our work, including sublinear-time algorithms for matching high degree vertices, random walks on directed expanders, and the monotone Even-Shiloach tree for dynamic shortest paths.
Partitioned Memory Storage Inspired Few-Shot Class-Incremental learning
Venue: arXiv cs.AI 2025
Year: 2025

Current mainstream deep learning techniques exhibit an over-reliance on extensive training data and a lack of adaptability to the dynamic world, marking a considerable disparity from human intelligence. To bridge this gap, Few-Shot Class-Incremental Learning (FSCIL) has emerged, focusing on continuous learning of new categories with limited samples without forgetting old knowledge. Existing FSCIL studies typically use a single model to learn knowledge across all sessions, inevitably leading to the stability-plasticity dilemma. Unlike machines, humans store varied knowledge in different cerebral cortices. Inspired by this characteristic, our paper aims to develop a method that learns independent models for each session. It can inherently prevent catastrophic forgetting. During the testing stage, our method integrates Uncertainty Quantification (UQ) for model deployment. Our method provides a fresh viewpoint for FSCIL and demonstrates the state-of-the-art performance on CIFAR-100 and mini-ImageNet datasets.
Adept: Annotation-Denoising Auxiliary Tasks with Discrete Cosine Transform Map and Keypoint for Human-Centric Pretraining
Venue: arXiv cs.CV 2025
Year: 2025

Human-centric perception is the core of diverse computer vision tasks and has been a long-standing research focus. However, previous research studied these human-centric tasks individually, whose performance is largely limited to the size of the public task-specific datasets. Recent human-centric methods leverage the additional modalities, e.g., depth, to learn fine-grained semantic information, which limits the benefit of pretraining models due to their sensitivity to camera views and the scarcity of RGB-D data on the Internet. This paper improves the data scalability of human-centric pretraining methods by discarding depth information and exploring semantic information of RGB images in the frequency space by Discrete Cosine Transform (DCT). We further propose new annotation denoising auxiliary tasks with keypoints and DCT maps to enforce the RGB image extractor to learn fine-grained semantic information of human bodies. Our extensive experiments show that when pretrained on large-scale datasets (COCO and AIC datasets) without depth annotation, our model achieves better performance than state-of-the-art methods by +0.5 mAP on COCO, +1.4 PCKh on MPII and -0.51 EPE on Human3.6M for pose estimation, by +4.50 mIoU on Human3.6M for human parsing, by -3.14 MAE on SHA and -0.07 MAE on SHB for crowd counting, by +1.1 F1 score on SHA and +0.8 F1 score on SHA for crowd localization, and by +0.1 mAP on Market1501 and +0.8 mAP on MSMT for person ReID. We also validate the effectiveness of our method on MPII+NTURGBD datasets
DP-SMOTE: Integrating Differential Privacy and Oversampling Technique to Preserve Privacy in Smart Homes
Venue: arXiv cs.CR 2025
Year: 2025

Smart homes represent intelligent environments where interconnected devices gather information, enhancing users living experiences by ensuring comfort, safety, and efficient energy management. To enhance the quality of life, companies in the smart device industry collect user data, including activities, preferences, and power consumption. However, sharing such data necessitates privacy-preserving practices. This paper introduces a robust method for secure sharing of data to service providers, grounded in differential privacy (DP). This empowers smart home residents to contribute usage statistics while safeguarding their privacy. The approach incorporates the Synthetic Minority Oversampling technique (SMOTe) and seamlessly integrates Gaussian noise to generate synthetic data, enabling data and statistics sharing while preserving individual privacy. The proposed method employs the SMOTe algorithm and applies Gaussian noise to generate data. Subsequently, it employs a k-anonymity function to assess reidentification risk before sharing the data. The simulation outcomes demonstrate that our method delivers strong performance in safeguarding privacy and in accuracy, recall, and f-measure metrics. This approach is particularly effective in smart homes, offering substantial utility in privacy at a reidentification risk of 30%, with Gaussian noise set to 0.3, SMOTe at 500%, and the application of a k-anonymity function with k = 2. Additionally, it shows a high classification accuracy, ranging from 90% to 98%, across various classification techniques.